vemem — visual entity memory

Quick check — is vemem available?

Run python -c "import vemem; print(vemem.__version__)". If that fails, install:

pip install vemem
or with uv:
uv pip install vemem

First-time face encoding triggers a ~200MB InsightFace model download into ~/.insightface/. Warn the user if their network is constrained.

Run the MCP server (preferred for agents)

python -m vemem.mcp_server

This exposes 14 tools over stdio. Wire it into your host's MCP config. For Claude Desktop, the ready-to-paste config lives at docs/examples/claude_desktop_config.json in the vemem repo.

Use directly (preferred for scripting in Python)

from vemem import Vemem
vem = Vemem()  # LanceDB store at ~/.vemem, InsightFace encoder

Store path is overridable via VEMEM_HOME env var or Vemem(home="/path/to/store").

| Symptom | Cause | Fix | |---|---|---| | identify() returns [] on a face you labeled earlier | Different encoder version, or the face isn't being detected | Check encoder.id hasn't changed; try min_confidence=0.2 to see raw scores | | RuntimeError: image pipeline unavailable | InsightFace weights not installed | First call downloads ~200MB from InsightFace to ~/.insightface/; ensure network access on first use | | ModalityMismatchError on merge | Trying to merge a face entity with an object entity | v0 keeps modalities separate; create an instance_of relationship instead | | OperationNotReversibleError on undo | Past 30 days, or op was forget | Not fixable — forget is deliberately irreversible; window is configurable via DEFAULT_UNDO_WINDOW |